Magnet hypothesis: plant-pollinator interactions

Purpose: A test of the magnet hypothesis was examined in Mojave National Preserve by Ally Ruttan.

Hypothesis: Floral resource island created by shrubs and the associated beneficiary annual plants will positively and non-additively influence pollinator visitation rates.

Predictions:
(1) The frequency and duration of pollinator visitations to annuals is greater under shrubs than in the paired-open microsites (magnet H because of concentration).
(2) Annual plants under flowering entomophilous shrubs (Larrea tridentata) will have a higher frequency and duration of pollinator visitations than annual plants under anemophilous shrubs (Ambrosia dumosa) because of higher concentrations of suitable floral resources for pollinators (specificity of pollinator faciliation).
(3) Shrubs with annuals in their understory will have a higher frequency and duration of pollinator visitations than shrubs without annuals due to increased concentrations of floral resources for pollinators (reverse magnet effect and reciprocal benefits).
(4) Sites with both shrubs and annuals will have the highest frequency and duration of pollinator visitations to both the shrubs and the annuals (i.e. annuals under shrubs also with flowers are visited the most).

An interesting corollary is that there are appropriate floral resources for desert pollinators, that they discriminate, and that entomophilous and anemophilous shrubs facilitate flowering similarly.

Data wrangling

#libraries
library(tidyverse)
library(DT)
library(lubridate)

#meta-data
meta <- read_csv("data/meta-data.csv")
datatable(meta)
#data
data.2015 <- read_csv("data/MNP.2015.csv")
data.2016 <- read_csv("data/MNP.2016.csv")

#merge
data <- rbind.data.frame(data.2015, data.2016)
data <- data %>% rename(net.treatment = treatment) %>% na.omit(data) 
data$year <- as.character(data$year)
data$rep <- as.character(data$rep)

#tidy data to expand treatment column (current structure is a mix of three factors)
#microsite
data <- data %>% mutate(microsite = ifelse(net.treatment %in% c("SA", "SAA", "SX"), "Larrea", ifelse(net.treatment %in% c("OA"), "open", ifelse(net.treatment %in% c("AMB"), "Ambrosia", NA))))
length(unique(data$microsite))
## [1] 3
#annuals present
data <- data %>% mutate(annuals = ifelse(net.treatment %in% c("SA", "SAA"), "annuals", ifelse(net.treatment %in% c("OA"), "annuals", ifelse(net.treatment %in% c("AMB"), "annuals", "none"))))
length(unique(data$annuals))
## [1] 2
#target flowers
data <- data %>% mutate(target.flowers = ifelse(net.treatment %in% c("SA", "SX"), "shrub flowers", ifelse(net.treatment %in% c("AMB", "SAA", "OA"), "annual plant flowers", "NA")))
length(unique(data$annuals))
## [1] 2
#frequency counts in a separate dataframe (weighted by duration of recording)
frequency <- data %>% group_by(year, day, net.treatment, rep, microsite, annuals, target.flowers) %>% summarise(net.time = sum(total.duration), mean.visitation.duration = mean(visitation.duration), mean.floral.density = mean(floral.density), mean.insect.RTU = n_distinct(insect.RTU), count = n())

frequency$net.time <- as.numeric(frequency$net.time) #converts to total seconds
frequency$mean.visitation.duration <- as.numeric(frequency$mean.visitation.duration) #converts to total seconds
frequency$count <- as.numeric(frequency$count)

frequency$rate <- as.numeric(frequency$count)/frequency$net.time #weight by net time
frequency$proportion.visitations <- frequency$mean.visitation.duration/frequency$net.time
frequency$rate.per.flower <- frequency$count/frequency$mean.floral.density
datatable(frequency)
#FILTER OUT ANNUALS VERSUS SHRUB FLOWERS
frequency.annuals <- frequency %>% filter(target.flowers == "annual plant flowers")
frequency.shrubs <- frequency %>% filter(target.flowers == "shrub flowers")

#repeat above with RTU as factor
frequency.by.RTU <- data %>% group_by(year, day, net.treatment, insect.RTU, rep, microsite, annuals, target.flowers) %>% summarise(net.time = sum(total.duration), mean.visitation.duration = mean(visitation.duration), mean.floral.density = mean(floral.density), count = n())

frequency.by.RTU$net.time <- as.numeric(frequency.by.RTU$net.time) #converts to total seconds
frequency.by.RTU$mean.visitation.duration <- as.numeric(frequency.by.RTU$mean.visitation.duration) #converts to total seconds
frequency.by.RTU$count <- as.numeric(frequency.by.RTU$count)

frequency.by.RTU$rate <- as.numeric(frequency.by.RTU$count)/frequency.by.RTU$net.time #weight by net time
frequency.by.RTU$proportion.visitations <- frequency.by.RTU$mean.visitation.duration/frequency.by.RTU$net.time
frequency.by.RTU$rate.per.flower <- frequency.by.RTU$count/frequency.by.RTU$mean.floral.density
datatable(frequency.by.RTU)
#repeat above but with RTU as species diversity response

#split out by year because of non-orthogonality
freq.2015 <- frequency %>% filter(year == 2015)
freq.2016 <- frequency %>% filter(year == 2016)

Data visualization

#Ideal figures for publication.
#Two figures total: 1(a) rate, (b) duration and 2(a) rate per RTU and (b) mean visitation rate per RTU - maybe I think there are other options.  ADD insect.RTU richness

#Higher-order treatment patterns in frequency####
#Collapsed single factor model
ggplot(frequency, aes(net.treatment, rate)) + geom_boxplot() + ylab("visitation rate") + facet_wrap(~year)

ggplot(frequency, aes(net.treatment, count)) + geom_boxplot() + ylab("visitations") + facet_wrap(~year)

ggplot(frequency, aes(net.treatment, rate.per.flower)) + geom_boxplot() + ylab("visitations per flower") + facet_wrap(~year)

ggplot(frequency, aes(net.treatment, proportion.visitations)) + geom_boxplot() + ylab("mean duration of visit per total duration recorded") + facet_wrap(~year)

ggplot(frequency, aes(net.treatment, mean.insect.RTU)) + geom_boxplot() + ylab("mean RTU richness") + facet_wrap(~year)

#net treatment per RTU
ggplot(frequency.by.RTU, aes(net.treatment, rate)) + geom_boxplot() + ylab("visitation rate") + facet_wrap(~insect.RTU*year)

ggplot(frequency.by.RTU, aes(net.treatment, proportion.visitations)) + geom_boxplot() + ylab("mean duration of visit per total duration recorded") + facet_wrap(~insect.RTU*year)

#Treatments separated
ggplot(frequency, aes(microsite, rate, fill = target.flowers)) + geom_boxplot() + facet_wrap(~year) + scale_fill_brewer(palette = "YlGn")

ggplot(frequency, aes(microsite, count, fill = target.flowers)) + geom_boxplot() + facet_wrap(~year) + scale_fill_brewer(palette = "YlGn")

ggplot(frequency, aes(microsite, count, weight = net.time, fill = target.flowers)) + geom_histogram(stat="identity") + facet_wrap(~year) + scale_fill_brewer(palette = "YlGn")

ggplot(frequency, aes(microsite, rate.per.flower, fill = target.flowers)) + geom_boxplot() + facet_wrap(~year) + scale_fill_brewer(palette = "YlGn")

ggplot(frequency, aes(microsite, proportion.visitations, fill = target.flowers)) + geom_boxplot() + facet_wrap(~year) + scale_fill_brewer(palette = "YlGn")

ggplot(frequency, aes(microsite, mean.insect.RTU, fill = target.flowers)) + geom_boxplot() + facet_wrap(~year) + scale_fill_brewer(palette = "YlGn")

#relationships with sampling effort
ggplot(frequency, aes(net.time, count, color = year)) + geom_point()

ggplot(frequency, aes(net.time, count, color = year)) + geom_point() + facet_wrap(~microsite)

#floral density
ggplot(frequency, aes(mean.floral.density, rate, color = microsite)) + geom_point() + facet_wrap(~year)

ggplot(frequency, aes(mean.floral.density, proportion.visitations, color = microsite)) + geom_point() + facet_wrap(~year)

#relationships with sampling effort
ggplot(frequency, aes(net.time, rate, color = year)) + geom_point() + facet_wrap(~microsite*target.flowers)

ggplot(frequency, aes(net.time, mean.visitation.duration, color = year)) + geom_point() + facet_wrap(~microsite*target.flowers)

EDA

#test distributions and explore outliers
summary(frequency)
##      year               day            net.treatment     
##  Length:266         Length:266         Length:266        
##  Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character  
##                                                          
##                                                          
##                                                          
##      rep             microsite           annuals         
##  Length:266         Length:266         Length:266        
##  Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character  
##                                                          
##                                                          
##                                                          
##  target.flowers        net.time      mean.visitation.duration
##  Length:266         Min.   :   300   Min.   :   0.000        
##  Class :character   1st Qu.:  4500   1st Qu.:   1.475        
##  Mode  :character   Median : 23010   Median :  26.248        
##                     Mean   : 64855   Mean   :  58.067        
##                     3rd Qu.:100660   3rd Qu.:  47.584        
##                     Max.   :542929   Max.   :2484.114        
##  mean.floral.density mean.insect.RTU     count             rate          
##  Min.   :  5.00      Min.   :1.000   Min.   :  1.00   Min.   :0.0001774  
##  1st Qu.: 23.13      1st Qu.:2.000   1st Qu.:  3.00   1st Qu.:0.0002130  
##  Median : 71.26      Median :3.000   Median :  8.00   Median :0.0002780  
##  Mean   : 98.74      Mean   :3.342   Mean   : 15.14   Mean   :0.0005540  
##  3rd Qu.:200.00      3rd Qu.:4.000   3rd Qu.: 22.00   3rd Qu.:0.0011111  
##  Max.   :200.00      Max.   :9.000   Max.   :109.00   Max.   :0.0033333  
##  proportion.visitations rate.per.flower  
##  Min.   :0.000e+00      Min.   :0.00500  
##  1st Qu.:9.565e-05      1st Qu.:0.02134  
##  Median :4.459e-04      Median :0.10158  
##  Mean   :3.221e-03      Mean   :0.62738  
##  3rd Qu.:1.633e-03      3rd Qu.:0.86012  
##  Max.   :2.117e-01      Max.   :6.05000
require(fitdistrplus)
descdist(freq.2015$rate, boot = 1000)

## summary statistics
## ------
## min:  0.0001873882   max:  0.001337793 
## median:  0.0003267998 
## mean:  0.0004887686 
## estimated sd:  0.0003435598 
## estimated skewness:  1.110484 
## estimated kurtosis:  2.810992
descdist(freq.2016$rate, boot = 1000)

## summary statistics
## ------
## min:  0.0001773679   max:  0.003333333 
## median:  0.0002480947 
## mean:  0.0006144842 
## estimated sd:  0.000504536 
## estimated skewness:  1.331961 
## estimated kurtosis:  7.146892
detach("package:fitdistrplus", unload = TRUE)

#explore temperature on count and mean visitation rates

Models

#GLM for count and weight by net.time (alt - use MASS and glm.nb)
#library(MASS) #need for glm.nb

#all codes aggregated
#2015 counts
m <- glm(count~net.treatment + mean.floral.density, family = "poisson", weight = net.time, data = freq.2015)
anova(m, test = "Chisq") 
## Analysis of Deviance Table
## 
## Model: poisson, link: log
## 
## Response: count
## 
## Terms added sequentially (first to last)
## 
## 
##                     Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                  127   47238655              
## net.treatment        3 13736996       124   33501659 < 2.2e-16 ***
## mean.floral.density  1 13630462       123   19871197 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#posthoc test
require(lsmeans)
lsmeans(m, pairwise~net.treatment, adjust="tukey")
## $lsmeans
##  net.treatment   lsmean           SE df asymp.LCL asymp.UCL
##  OA            2.045110 0.0004169528 NA  2.044293  2.045927
##  SA            2.614917 0.0005830291 NA  2.613774  2.616060
##  SAA           1.736211 0.0004255844 NA  1.735377  1.737045
##  SX            1.354487 0.0007356275 NA  1.353045  1.355929
## 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast   estimate           SE df  z.ratio p.value
##  OA - SA  -0.5698067 0.0007644926 NA -745.340  <.0001
##  OA - SAA  0.3088991 0.0002022540 NA 1527.283  <.0001
##  OA - SX   0.6906229 0.0007383659 NA  935.340  <.0001
##  SA - SAA  0.8787058 0.0007683106 NA 1143.686  <.0001
##  SA - SX   1.2604296 0.0009583930 NA 1315.149  <.0001
##  SAA - SX  0.3817238 0.0007455663 NA  511.992  <.0001
## 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 4 estimates
#2016 counts
m <- glm(count~net.treatment + mean.floral.density, family = "poisson", weight = net.time, data = freq.2016)
anova(m, test = "Chisq") 
## Analysis of Deviance Table
## 
## Model: poisson, link: log
## 
## Response: count
## 
## Terms added sequentially (first to last)
## 
## 
##                     Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                  137  174945050              
## net.treatment        4 15411680       133  159533369 < 2.2e-16 ***
## mean.floral.density  1  8872062       132  150661307 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#posthoc test
require(lsmeans)
lsmeans(m, pairwise~net.treatment, adjust="tukey")
## $lsmeans
##  net.treatment     lsmean           SE df  asymp.LCL  asymp.UCL
##  AMB            5.2074833 0.0005064262 NA  5.2064907  5.2084759
##  OA             5.2481438 0.0004770340 NA  5.2472089  5.2490788
##  SA            -1.0445768 0.0023971369 NA -1.0492751 -1.0398785
##  SAA            5.3522565 0.0004641314 NA  5.3513468  5.3531661
##  SX            -0.7880837 0.0019263237 NA -0.7918592 -0.7843082
## 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast     estimate           SE df   z.ratio p.value
##  AMB - OA  -0.04066053 1.176853e-04 NA  -345.502  <.0001
##  AMB - SA   6.25206012 2.597272e-03 NA  2407.164  <.0001
##  AMB - SAA -0.14477316 1.130315e-04 NA -1280.821  <.0001
##  AMB - SX   5.99556701 2.170318e-03 NA  2762.529  <.0001
##  OA - SA    6.29272065 2.583621e-03 NA  2435.621  <.0001
##  OA - SAA  -0.10411263 9.853844e-05 NA -1056.569  <.0001
##  OA - SX    6.03622754 2.153962e-03 NA  2802.383  <.0001
##  SA - SAA  -6.39683328 2.578069e-03 NA -2481.250  <.0001
##  SA - SX   -0.25649310 2.889407e-03 NA   -88.770  <.0001
##  SAA - SX   6.14034017 2.147300e-03 NA  2859.563  <.0001
## 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 5 estimates
#repeat above for mean.visitation.duration
#2015
m <- glm(mean.visitation.duration~net.treatment + mean.floral.density, family = "gaussian", weight = net.time, data = freq.2015)
anova(m, test = "Chisq") 
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: mean.visitation.duration
## 
## Terms added sequentially (first to last)
## 
## 
##                     Df   Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                    127 7.3579e+10              
## net.treatment        3 1.1426e+10       124 6.2153e+10 4.864e-05 ***
## mean.floral.density  1 1.5637e+06       123 6.2151e+10    0.9556    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#posthoc test
require(lsmeans)
lsmeans(m, pairwise~net.treatment, adjust="tukey")
## $lsmeans
##  net.treatment     lsmean       SE df asymp.LCL asymp.UCL
##  OA             71.412514 29.25204 NA  14.07957 128.74546
##  SA              5.832637 40.04779 NA -72.65959  84.32486
##  SAA           145.904619 29.89835 NA  87.30492 204.50431
##  SX             12.115824 39.10838 NA -64.53519  88.76684
## 
## Results are given on the identity (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast    estimate       SE df z.ratio p.value
##  OA - SA    65.579877 56.58232 NA   1.159  0.6528
##  OA - SAA  -74.492105 21.63099 NA  -3.444  0.0032
##  OA - SX    59.296690 47.54971 NA   1.247  0.5968
##  SA - SAA -140.071982 56.69949 NA  -2.470  0.0646
##  SA - SX    -6.283187 56.59297 NA  -0.111  0.9995
##  SAA - SX  133.788795 47.99355 NA   2.788  0.0272
## 
## P value adjustment: tukey method for comparing a family of 4 estimates
#2016
m <- glm(mean.visitation.duration~net.treatment + mean.floral.density, family = "gaussian", weight = net.time, data = freq.2016)
anova(m, test = "Chisq") 
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: mean.visitation.duration
## 
## Terms added sequentially (first to last)
## 
## 
##                     Df   Deviance Resid. Df Resid. Dev Pr(>Chi)  
## NULL                                    137 1.3624e+12           
## net.treatment        4 1.0011e+11       133 1.2623e+12  0.03313 *
## mean.floral.density  1 8.5266e+08       132 1.2614e+12  0.76516  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#posthoc test
require(lsmeans)
lsmeans(m, pairwise~net.treatment, adjust="tukey")
## $lsmeans
##  net.treatment    lsmean       SE df  asymp.LCL asymp.UCL
##  AMB           -60.98750 348.6464 NA  -744.3219  622.3469
##  OA            136.40291 330.4878 NA  -511.3413  784.1471
##  SA            167.37093 649.1859 NA -1105.0100 1439.7519
##  SAA           -59.88931 322.6658 NA  -692.3026  572.5240
##  SX            183.58414 622.0679 NA -1035.6465 1402.8148
## 
## Results are given on the identity (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast     estimate        SE df z.ratio p.value
##  AMB - OA  -197.390411  75.65485 NA  -2.609  0.0687
##  AMB - SA  -228.358432 945.87912 NA  -0.241  0.9993
##  AMB - SAA   -1.098192  73.96065 NA  -0.015  1.0000
##  AMB - SX  -244.571636 927.47694 NA  -0.264  0.9989
##  OA - SA    -30.968021 929.59326 NA  -0.033  1.0000
##  OA - SAA   196.292219  67.38350 NA   2.913  0.0295
##  OA - SX    -47.181225 910.86211 NA  -0.052  1.0000
##  SA - SAA   227.260240 922.95167 NA   0.246  0.9992
##  SA - SX    -16.213203 535.98700 NA  -0.030  1.0000
##  SAA - SX  -243.473443 904.08293 NA  -0.269  0.9988
## 
## P value adjustment: tukey method for comparing a family of 5 estimates
#2015 visitation rates 
#suggestion here to use offset instead ratios http://stats.stackexchange.com/questions/164889/how-to-deal-with-non-integer-warning-from-negative-binomial-glm
m <- glm(rate~net.treatment + mean.floral.density, family = "poisson", data = freq.2015)
anova(m, test = "Chisq") 
## Analysis of Deviance Table
## 
## Model: poisson, link: log
## 
## Response: rate
## 
## Terms added sequentially (first to last)
## 
## 
##                     Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                                  127   0.027126         
## net.treatment        3 0.010299       124   0.016827   0.9997
## mean.floral.density  1 0.013141       123   0.003686   0.9087
#posthoc test
require(lsmeans)
lsmeans(m, pairwise~net.treatment, adjust="tukey")
## $lsmeans
##  net.treatment    lsmean        SE df asymp.LCL asymp.UCL
##  OA            -7.789261 10.543074 NA -28.45331 12.874785
##  SA            -7.945378  9.027243 NA -25.63845  9.747692
##  SAA           -7.749521 10.385659 NA -28.10504 12.605997
##  SX            -7.808312  8.749320 NA -24.95666  9.340040
## 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast    estimate        SE df z.ratio p.value
##  OA - SA   0.15611760 14.552411 NA   0.011  1.0000
##  OA - SAA -0.03973967 14.600525 NA  -0.003  1.0000
##  OA - SX   0.01905075 14.343893 NA   0.001  1.0000
##  SA - SAA -0.19585727 14.522862 NA  -0.013  1.0000
##  SA - SX  -0.13706685  9.564184 NA  -0.014  1.0000
##  SAA - SX  0.05879042 14.309087 NA   0.004  1.0000
## 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 4 estimates
#2016 visitation rates 
m <- glm(rate~net.treatment + mean.floral.density, family = "poisson", data = freq.2016)
anova(m, test = "Chisq") 
## Analysis of Deviance Table
## 
## Model: poisson, link: log
## 
## Response: rate
## 
## Terms added sequentially (first to last)
## 
## 
##                     Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                                  137   0.052547         
## net.treatment        4  0.03685       133   0.015697   0.9998
## mean.floral.density  1  0.00108       132   0.014616   0.9738
#posthoc test
require(lsmeans)
lsmeans(m, pairwise~net.treatment, adjust="tukey")
## $lsmeans
##  net.treatment     lsmean        SE df asymp.LCL asymp.UCL
##  AMB           -10.467429  75.20060 NA -157.8579  136.9230
##  OA            -10.753074  73.11079 NA -154.0476  132.5414
##  SA             -3.300373 107.76956 NA -214.5248  207.9241
##  SAA           -10.333146  72.70696 NA -152.8362  132.1699
##  SX             -3.300373 107.77508 NA -214.5356  207.9349
## 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast       estimate         SE df z.ratio p.value
##  AMB - OA   2.856451e-01  16.803027 NA   0.017  1.0000
##  AMB - SA  -7.167055e+00 182.447513 NA  -0.039  1.0000
##  AMB - SAA -1.342829e-01  15.361477 NA  -0.009  1.0000
##  AMB - SX  -7.167055e+00 182.450775 NA  -0.039  1.0000
##  OA - SA   -7.452700e+00 180.165095 NA  -0.041  1.0000
##  OA - SAA  -4.199280e-01  16.493361 NA  -0.025  1.0000
##  OA - SX   -7.452700e+00 180.168399 NA  -0.041  1.0000
##  SA - SAA   7.032772e+00 179.951299 NA   0.039  1.0000
##  SA - SX    7.993606e-15   8.091736 NA   0.000  1.0000
##  SAA - SX  -7.032772e+00 179.954607 NA  -0.039  1.0000
## 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 5 estimates
#treatments split out
#2015
m <- glm(mean.visitation.duration~microsite*annuals*target.flowers + mean.floral.density, family = "gaussian", weight = net.time, data = freq.2015)
anova(m, test = "Chisq") 
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: mean.visitation.duration
## 
## Terms added sequentially (first to last)
## 
## 
##                                  Df   Deviance Resid. Df Resid. Dev
## NULL                                                 127 7.3579e+10
## microsite                         1 1712419588       126 7.1867e+10
## annuals                           1 3593435174       125 6.8273e+10
## target.flowers                    1 6120107085       124 6.2153e+10
## mean.floral.density               1    1563658       123 6.2151e+10
## microsite:annuals                 0          0       123 6.2151e+10
## microsite:target.flowers          0          0       123 6.2151e+10
## annuals:target.flowers            0          0       123 6.2151e+10
## microsite:annuals:target.flowers  0          0       123 6.2151e+10
##                                  Pr(>Chi)    
## NULL                                         
## microsite                        0.065635 .  
## annuals                          0.007659 ** 
## target.flowers                   0.000501 ***
## mean.floral.density              0.955638    
## microsite:annuals                            
## microsite:target.flowers                     
## annuals:target.flowers                       
## microsite:annuals:target.flowers             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lsmeans(m, pairwise~microsite*annuals*target.flowers, adjust="tukey")
## $lsmeans
##  microsite annuals target.flowers           lsmean       SE df asymp.LCL
##  Larrea    annuals annual plant flowers 145.904619 29.89835 NA  87.30492
##  open      annuals annual plant flowers  71.412514 29.25204 NA  14.07957
##  Larrea    none    annual plant flowers         NA       NA NA        NA
##  open      none    annual plant flowers         NA       NA NA        NA
##  Larrea    annuals shrub flowers          5.832637 40.04779 NA -72.65959
##  open      annuals shrub flowers                NA       NA NA        NA
##  Larrea    none    shrub flowers         12.115824 39.10838 NA -64.53519
##  open      none    shrub flowers                NA       NA NA        NA
##  asymp.UCL
##  204.50431
##  128.74546
##         NA
##         NA
##   84.32486
##         NA
##   88.76684
##         NA
## 
## Results are given on the identity (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                                                               
##  Larrea,annuals,annual plant flowers - open,annuals,annual plant flowers
##  Larrea,annuals,annual plant flowers - Larrea,none,annual plant flowers 
##  Larrea,annuals,annual plant flowers - open,none,annual plant flowers   
##  Larrea,annuals,annual plant flowers - Larrea,annuals,shrub flowers     
##  Larrea,annuals,annual plant flowers - open,annuals,shrub flowers       
##  Larrea,annuals,annual plant flowers - Larrea,none,shrub flowers        
##  Larrea,annuals,annual plant flowers - open,none,shrub flowers          
##  open,annuals,annual plant flowers - Larrea,none,annual plant flowers   
##  open,annuals,annual plant flowers - open,none,annual plant flowers     
##  open,annuals,annual plant flowers - Larrea,annuals,shrub flowers       
##  open,annuals,annual plant flowers - open,annuals,shrub flowers         
##  open,annuals,annual plant flowers - Larrea,none,shrub flowers          
##  open,annuals,annual plant flowers - open,none,shrub flowers            
##  Larrea,none,annual plant flowers - open,none,annual plant flowers      
##  Larrea,none,annual plant flowers - Larrea,annuals,shrub flowers        
##  Larrea,none,annual plant flowers - open,annuals,shrub flowers          
##  Larrea,none,annual plant flowers - Larrea,none,shrub flowers           
##  Larrea,none,annual plant flowers - open,none,shrub flowers             
##  open,none,annual plant flowers - Larrea,annuals,shrub flowers          
##  open,none,annual plant flowers - open,annuals,shrub flowers            
##  open,none,annual plant flowers - Larrea,none,shrub flowers             
##  open,none,annual plant flowers - open,none,shrub flowers               
##  Larrea,annuals,shrub flowers - open,annuals,shrub flowers              
##  Larrea,annuals,shrub flowers - Larrea,none,shrub flowers               
##  Larrea,annuals,shrub flowers - open,none,shrub flowers                 
##  open,annuals,shrub flowers - Larrea,none,shrub flowers                 
##  open,annuals,shrub flowers - open,none,shrub flowers                   
##  Larrea,none,shrub flowers - open,none,shrub flowers                    
##    estimate       SE df z.ratio p.value
##   74.492105 21.63099 NA   3.444  0.0134
##          NA       NA NA      NA      NA
##          NA       NA NA      NA      NA
##  140.071982 56.69949 NA   2.470  0.2081
##          NA       NA NA      NA      NA
##  133.788795 47.99355 NA   2.788  0.0980
##          NA       NA NA      NA      NA
##          NA       NA NA      NA      NA
##          NA       NA NA      NA      NA
##   65.579877 56.58232 NA   1.159  0.9433
##          NA       NA NA      NA      NA
##   59.296690 47.54971 NA   1.247  0.9177
##          NA       NA NA      NA      NA
##          NA       NA NA      NA      NA
##          NA       NA NA      NA      NA
##          NA       NA NA      NA      NA
##          NA       NA NA      NA      NA
##          NA       NA NA      NA      NA
##          NA       NA NA      NA      NA
##          NA       NA NA      NA      NA
##          NA       NA NA      NA      NA
##          NA       NA NA      NA      NA
##          NA       NA NA      NA      NA
##   -6.283187 56.59297 NA  -0.111  1.0000
##          NA       NA NA      NA      NA
##          NA       NA NA      NA      NA
##          NA       NA NA      NA      NA
##          NA       NA NA      NA      NA
## 
## P value adjustment: tukey method for comparing a family of 8 estimates
#2016
m <- glm(mean.visitation.duration~microsite*annuals*target.flowers + mean.floral.density, family = "gaussian", weight = net.time, data = freq.2016)
anova(m, test = "Chisq") 
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: mean.visitation.duration
## 
## Terms added sequentially (first to last)
## 
## 
##                                  Df   Deviance Resid. Df Resid. Dev
## NULL                                                 137 1.3624e+12
## microsite                         2 1.0009e+11       135 1.2623e+12
## annuals                           1 1.3266e+06       134 1.2623e+12
## target.flowers                    1 2.5106e+07       133 1.2623e+12
## mean.floral.density               1 8.5266e+08       132 1.2614e+12
## microsite:annuals                 0 0.0000e+00       132 1.2614e+12
## microsite:target.flowers          0 0.0000e+00       132 1.2614e+12
## annuals:target.flowers            0 0.0000e+00       132 1.2614e+12
## microsite:annuals:target.flowers  0 0.0000e+00       132 1.2614e+12
##                                  Pr(>Chi)   
## NULL                                        
## microsite                        0.005318 **
## annuals                          0.990599   
## target.flowers                   0.959121   
## mean.floral.density              0.765163   
## microsite:annuals                           
## microsite:target.flowers                    
## annuals:target.flowers                      
## microsite:annuals:target.flowers            
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lsmeans(m, pairwise~microsite*annuals*target.flowers, adjust="tukey")
## $lsmeans
##  microsite annuals target.flowers          lsmean       SE df  asymp.LCL
##  Ambrosia  annuals annual plant flowers -60.98750 348.6464 NA  -744.3219
##  Larrea    annuals annual plant flowers -59.88931 322.6658 NA  -692.3026
##  open      annuals annual plant flowers 136.40291 330.4878 NA  -511.3413
##  Ambrosia  none    annual plant flowers        NA       NA NA         NA
##  Larrea    none    annual plant flowers        NA       NA NA         NA
##  open      none    annual plant flowers        NA       NA NA         NA
##  Ambrosia  annuals shrub flowers               NA       NA NA         NA
##  Larrea    annuals shrub flowers        167.37093 649.1859 NA -1105.0100
##  open      annuals shrub flowers               NA       NA NA         NA
##  Ambrosia  none    shrub flowers               NA       NA NA         NA
##  Larrea    none    shrub flowers        183.58414 622.0679 NA -1035.6465
##  open      none    shrub flowers               NA       NA NA         NA
##  asymp.UCL
##   622.3469
##   572.5240
##   784.1471
##         NA
##         NA
##         NA
##         NA
##  1439.7519
##         NA
##         NA
##  1402.8148
##         NA
## 
## Results are given on the identity (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                                                                   
##  Ambrosia,annuals,annual plant flowers - Larrea,annuals,annual plant flowers
##  Ambrosia,annuals,annual plant flowers - open,annuals,annual plant flowers  
##  Ambrosia,annuals,annual plant flowers - Ambrosia,none,annual plant flowers 
##  Ambrosia,annuals,annual plant flowers - Larrea,none,annual plant flowers   
##  Ambrosia,annuals,annual plant flowers - open,none,annual plant flowers     
##  Ambrosia,annuals,annual plant flowers - Ambrosia,annuals,shrub flowers     
##  Ambrosia,annuals,annual plant flowers - Larrea,annuals,shrub flowers       
##  Ambrosia,annuals,annual plant flowers - open,annuals,shrub flowers         
##  Ambrosia,annuals,annual plant flowers - Ambrosia,none,shrub flowers        
##  Ambrosia,annuals,annual plant flowers - Larrea,none,shrub flowers          
##  Ambrosia,annuals,annual plant flowers - open,none,shrub flowers            
##  Larrea,annuals,annual plant flowers - open,annuals,annual plant flowers    
##  Larrea,annuals,annual plant flowers - Ambrosia,none,annual plant flowers   
##  Larrea,annuals,annual plant flowers - Larrea,none,annual plant flowers     
##  Larrea,annuals,annual plant flowers - open,none,annual plant flowers       
##  Larrea,annuals,annual plant flowers - Ambrosia,annuals,shrub flowers       
##  Larrea,annuals,annual plant flowers - Larrea,annuals,shrub flowers         
##  Larrea,annuals,annual plant flowers - open,annuals,shrub flowers           
##  Larrea,annuals,annual plant flowers - Ambrosia,none,shrub flowers          
##  Larrea,annuals,annual plant flowers - Larrea,none,shrub flowers            
##  Larrea,annuals,annual plant flowers - open,none,shrub flowers              
##  open,annuals,annual plant flowers - Ambrosia,none,annual plant flowers     
##  open,annuals,annual plant flowers - Larrea,none,annual plant flowers       
##  open,annuals,annual plant flowers - open,none,annual plant flowers         
##  open,annuals,annual plant flowers - Ambrosia,annuals,shrub flowers         
##  open,annuals,annual plant flowers - Larrea,annuals,shrub flowers           
##  open,annuals,annual plant flowers - open,annuals,shrub flowers             
##  open,annuals,annual plant flowers - Ambrosia,none,shrub flowers            
##  open,annuals,annual plant flowers - Larrea,none,shrub flowers              
##  open,annuals,annual plant flowers - open,none,shrub flowers                
##  Ambrosia,none,annual plant flowers - Larrea,none,annual plant flowers      
##  Ambrosia,none,annual plant flowers - open,none,annual plant flowers        
##  Ambrosia,none,annual plant flowers - Ambrosia,annuals,shrub flowers        
##  Ambrosia,none,annual plant flowers - Larrea,annuals,shrub flowers          
##  Ambrosia,none,annual plant flowers - open,annuals,shrub flowers            
##  Ambrosia,none,annual plant flowers - Ambrosia,none,shrub flowers           
##  Ambrosia,none,annual plant flowers - Larrea,none,shrub flowers             
##  Ambrosia,none,annual plant flowers - open,none,shrub flowers               
##  Larrea,none,annual plant flowers - open,none,annual plant flowers          
##  Larrea,none,annual plant flowers - Ambrosia,annuals,shrub flowers          
##  Larrea,none,annual plant flowers - Larrea,annuals,shrub flowers            
##  Larrea,none,annual plant flowers - open,annuals,shrub flowers              
##  Larrea,none,annual plant flowers - Ambrosia,none,shrub flowers             
##  Larrea,none,annual plant flowers - Larrea,none,shrub flowers               
##  Larrea,none,annual plant flowers - open,none,shrub flowers                 
##  open,none,annual plant flowers - Ambrosia,annuals,shrub flowers            
##  open,none,annual plant flowers - Larrea,annuals,shrub flowers              
##  open,none,annual plant flowers - open,annuals,shrub flowers                
##  open,none,annual plant flowers - Ambrosia,none,shrub flowers               
##  open,none,annual plant flowers - Larrea,none,shrub flowers                 
##  open,none,annual plant flowers - open,none,shrub flowers                   
##  Ambrosia,annuals,shrub flowers - Larrea,annuals,shrub flowers              
##  Ambrosia,annuals,shrub flowers - open,annuals,shrub flowers                
##  Ambrosia,annuals,shrub flowers - Ambrosia,none,shrub flowers               
##  Ambrosia,annuals,shrub flowers - Larrea,none,shrub flowers                 
##  Ambrosia,annuals,shrub flowers - open,none,shrub flowers                   
##  Larrea,annuals,shrub flowers - open,annuals,shrub flowers                  
##  Larrea,annuals,shrub flowers - Ambrosia,none,shrub flowers                 
##  Larrea,annuals,shrub flowers - Larrea,none,shrub flowers                   
##  Larrea,annuals,shrub flowers - open,none,shrub flowers                     
##  open,annuals,shrub flowers - Ambrosia,none,shrub flowers                   
##  open,annuals,shrub flowers - Larrea,none,shrub flowers                     
##  open,annuals,shrub flowers - open,none,shrub flowers                       
##  Ambrosia,none,shrub flowers - Larrea,none,shrub flowers                    
##  Ambrosia,none,shrub flowers - open,none,shrub flowers                      
##  Larrea,none,shrub flowers - open,none,shrub flowers                        
##     estimate        SE df z.ratio p.value
##    -1.098192  73.96065 NA  -0.015  1.0000
##  -197.390411  75.65485 NA  -2.609  0.2743
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##  -228.358432 945.87912 NA  -0.241  1.0000
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##  -244.571636 927.47694 NA  -0.264  1.0000
##           NA        NA NA      NA      NA
##  -196.292219  67.38350 NA  -2.913  0.1361
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##  -227.260240 922.95167 NA  -0.246  1.0000
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##  -243.473443 904.08293 NA  -0.269  1.0000
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##   -30.968021 929.59326 NA  -0.033  1.0000
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##   -47.181225 910.86211 NA  -0.052  1.0000
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##   -16.213203 535.98700 NA  -0.030  1.0000
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
## 
## P value adjustment: tukey method for comparing a family of 12 estimates
#2015
m <- glm(rate~microsite*annuals*target.flowers + mean.floral.density, family = "gaussian", weight = net.time, data = freq.2015)
anova(m, test = "Chisq") 
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: rate
## 
## Terms added sequentially (first to last)
## 
## 
##                                  Df Deviance Resid. Df Resid. Dev
## NULL                                               127   0.101279
## microsite                         1 0.006804       126   0.094475
## annuals                           1 0.004942       125   0.089533
## target.flowers                    1 0.028643       124   0.060890
## mean.floral.density               1 0.032269       123   0.028621
## microsite:annuals                 0 0.000000       123   0.028621
## microsite:target.flowers          0 0.000000       123   0.028621
## annuals:target.flowers            0 0.000000       123   0.028621
## microsite:annuals:target.flowers  0 0.000000       123   0.028621
##                                   Pr(>Chi)    
## NULL                                          
## microsite                        6.389e-08 ***
## annuals                          4.053e-06 ***
## target.flowers                   < 2.2e-16 ***
## mean.floral.density              < 2.2e-16 ***
## microsite:annuals                             
## microsite:target.flowers                      
## annuals:target.flowers                        
## microsite:annuals:target.flowers              
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lsmeans(m, pairwise~microsite*annuals*target.flowers, adjust="tukey")
## $lsmeans
##  microsite annuals target.flowers             lsmean           SE df
##  Larrea    annuals annual plant flowers 0.0004244116 2.028915e-05 NA
##  open      annuals annual plant flowers 0.0004264039 1.985056e-05 NA
##  Larrea    none    annual plant flowers           NA           NA NA
##  open      none    annual plant flowers           NA           NA NA
##  Larrea    annuals shrub flowers        0.0004181625 2.717661e-05 NA
##  open      annuals shrub flowers                  NA           NA NA
##  Larrea    none    shrub flowers        0.0004263407 2.653912e-05 NA
##  open      none    shrub flowers                  NA           NA NA
##     asymp.LCL    asymp.UCL
##  0.0003846456 0.0004641776
##  0.0003874975 0.0004653103
##            NA           NA
##            NA           NA
##  0.0003648973 0.0004714277
##            NA           NA
##  0.0003743250 0.0004783564
##            NA           NA
## 
## Results are given on the identity (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                                                               
##  Larrea,annuals,annual plant flowers - open,annuals,annual plant flowers
##  Larrea,annuals,annual plant flowers - Larrea,none,annual plant flowers 
##  Larrea,annuals,annual plant flowers - open,none,annual plant flowers   
##  Larrea,annuals,annual plant flowers - Larrea,annuals,shrub flowers     
##  Larrea,annuals,annual plant flowers - open,annuals,shrub flowers       
##  Larrea,annuals,annual plant flowers - Larrea,none,shrub flowers        
##  Larrea,annuals,annual plant flowers - open,none,shrub flowers          
##  open,annuals,annual plant flowers - Larrea,none,annual plant flowers   
##  open,annuals,annual plant flowers - open,none,annual plant flowers     
##  open,annuals,annual plant flowers - Larrea,annuals,shrub flowers       
##  open,annuals,annual plant flowers - open,annuals,shrub flowers         
##  open,annuals,annual plant flowers - Larrea,none,shrub flowers          
##  open,annuals,annual plant flowers - open,none,shrub flowers            
##  Larrea,none,annual plant flowers - open,none,annual plant flowers      
##  Larrea,none,annual plant flowers - Larrea,annuals,shrub flowers        
##  Larrea,none,annual plant flowers - open,annuals,shrub flowers          
##  Larrea,none,annual plant flowers - Larrea,none,shrub flowers           
##  Larrea,none,annual plant flowers - open,none,shrub flowers             
##  open,none,annual plant flowers - Larrea,annuals,shrub flowers          
##  open,none,annual plant flowers - open,annuals,shrub flowers            
##  open,none,annual plant flowers - Larrea,none,shrub flowers             
##  open,none,annual plant flowers - open,none,shrub flowers               
##  Larrea,annuals,shrub flowers - open,annuals,shrub flowers              
##  Larrea,annuals,shrub flowers - Larrea,none,shrub flowers               
##  Larrea,annuals,shrub flowers - open,none,shrub flowers                 
##  open,annuals,shrub flowers - Larrea,none,shrub flowers                 
##  open,annuals,shrub flowers - open,none,shrub flowers                   
##  Larrea,none,shrub flowers - open,none,shrub flowers                    
##       estimate           SE df z.ratio p.value
##  -1.992259e-06 1.467889e-05 NA  -0.136  1.0000
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##   6.249110e-06 3.847652e-05 NA   0.162  1.0000
##             NA           NA NA      NA      NA
##  -1.929057e-06 3.256863e-05 NA  -0.059  1.0000
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##   8.241369e-06 3.839701e-05 NA   0.215  1.0000
##             NA           NA NA      NA      NA
##   6.320196e-08 3.226744e-05 NA   0.002  1.0000
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##  -8.178167e-06 3.840424e-05 NA  -0.213  1.0000
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
## 
## P value adjustment: tukey method for comparing a family of 8 estimates
#2016
m <- glm(rate~microsite*annuals*target.flowers + mean.floral.density, family = "gaussian", weight = net.time, data = freq.2016)
anova(m, test = "Chisq") 
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: rate
## 
## Terms added sequentially (first to last)
## 
## 
##                                  Df Deviance Resid. Df Resid. Dev
## NULL                                               137   0.119429
## microsite                         2 0.001003       135   0.118426
## annuals                           1 0.059265       134   0.059161
## target.flowers                    1 0.047860       133   0.011301
## mean.floral.density               1 0.000211       132   0.011090
## microsite:annuals                 0 0.000000       132   0.011090
## microsite:target.flowers          0 0.000000       132   0.011090
## annuals:target.flowers            0 0.000000       132   0.011090
## microsite:annuals:target.flowers  0 0.000000       132   0.011090
##                                   Pr(>Chi)    
## NULL                                          
## microsite                         0.002555 ** 
## annuals                          < 2.2e-16 ***
## target.flowers                   < 2.2e-16 ***
## mean.floral.density               0.112618    
## microsite:annuals                             
## microsite:target.flowers                      
## annuals:target.flowers                        
## microsite:annuals:target.flowers              
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lsmeans(m, pairwise~microsite*annuals*target.flowers, adjust="tukey")
## $lsmeans
##  microsite annuals target.flowers             lsmean           SE df
##  Ambrosia  annuals annual plant flowers 0.0001734919 3.269023e-05 NA
##  Larrea    annuals annual plant flowers 0.0001604720 3.025420e-05 NA
##  open      annuals annual plant flowers 0.0001623359 3.098762e-05 NA
##  Ambrosia  none    annual plant flowers           NA           NA NA
##  Larrea    none    annual plant flowers           NA           NA NA
##  open      none    annual plant flowers           NA           NA NA
##  Ambrosia  annuals shrub flowers                  NA           NA NA
##  Larrea    annuals shrub flowers        0.0011870457 6.086980e-05 NA
##  open      annuals shrub flowers                  NA           NA NA
##  Ambrosia  none    shrub flowers                  NA           NA NA
##  Larrea    none    shrub flowers        0.0011870457 5.832713e-05 NA
##  open      none    shrub flowers                  NA           NA NA
##     asymp.LCL    asymp.UCL
##  0.0001094202 0.0002375635
##  0.0001011748 0.0002197691
##  0.0001016012 0.0002230705
##            NA           NA
##            NA           NA
##            NA           NA
##            NA           NA
##  0.0010677431 0.0013063483
##            NA           NA
##            NA           NA
##  0.0010727266 0.0013013648
##            NA           NA
## 
## Results are given on the identity (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                                                                   
##  Ambrosia,annuals,annual plant flowers - Larrea,annuals,annual plant flowers
##  Ambrosia,annuals,annual plant flowers - open,annuals,annual plant flowers  
##  Ambrosia,annuals,annual plant flowers - Ambrosia,none,annual plant flowers 
##  Ambrosia,annuals,annual plant flowers - Larrea,none,annual plant flowers   
##  Ambrosia,annuals,annual plant flowers - open,none,annual plant flowers     
##  Ambrosia,annuals,annual plant flowers - Ambrosia,annuals,shrub flowers     
##  Ambrosia,annuals,annual plant flowers - Larrea,annuals,shrub flowers       
##  Ambrosia,annuals,annual plant flowers - open,annuals,shrub flowers         
##  Ambrosia,annuals,annual plant flowers - Ambrosia,none,shrub flowers        
##  Ambrosia,annuals,annual plant flowers - Larrea,none,shrub flowers          
##  Ambrosia,annuals,annual plant flowers - open,none,shrub flowers            
##  Larrea,annuals,annual plant flowers - open,annuals,annual plant flowers    
##  Larrea,annuals,annual plant flowers - Ambrosia,none,annual plant flowers   
##  Larrea,annuals,annual plant flowers - Larrea,none,annual plant flowers     
##  Larrea,annuals,annual plant flowers - open,none,annual plant flowers       
##  Larrea,annuals,annual plant flowers - Ambrosia,annuals,shrub flowers       
##  Larrea,annuals,annual plant flowers - Larrea,annuals,shrub flowers         
##  Larrea,annuals,annual plant flowers - open,annuals,shrub flowers           
##  Larrea,annuals,annual plant flowers - Ambrosia,none,shrub flowers          
##  Larrea,annuals,annual plant flowers - Larrea,none,shrub flowers            
##  Larrea,annuals,annual plant flowers - open,none,shrub flowers              
##  open,annuals,annual plant flowers - Ambrosia,none,annual plant flowers     
##  open,annuals,annual plant flowers - Larrea,none,annual plant flowers       
##  open,annuals,annual plant flowers - open,none,annual plant flowers         
##  open,annuals,annual plant flowers - Ambrosia,annuals,shrub flowers         
##  open,annuals,annual plant flowers - Larrea,annuals,shrub flowers           
##  open,annuals,annual plant flowers - open,annuals,shrub flowers             
##  open,annuals,annual plant flowers - Ambrosia,none,shrub flowers            
##  open,annuals,annual plant flowers - Larrea,none,shrub flowers              
##  open,annuals,annual plant flowers - open,none,shrub flowers                
##  Ambrosia,none,annual plant flowers - Larrea,none,annual plant flowers      
##  Ambrosia,none,annual plant flowers - open,none,annual plant flowers        
##  Ambrosia,none,annual plant flowers - Ambrosia,annuals,shrub flowers        
##  Ambrosia,none,annual plant flowers - Larrea,annuals,shrub flowers          
##  Ambrosia,none,annual plant flowers - open,annuals,shrub flowers            
##  Ambrosia,none,annual plant flowers - Ambrosia,none,shrub flowers           
##  Ambrosia,none,annual plant flowers - Larrea,none,shrub flowers             
##  Ambrosia,none,annual plant flowers - open,none,shrub flowers               
##  Larrea,none,annual plant flowers - open,none,annual plant flowers          
##  Larrea,none,annual plant flowers - Ambrosia,annuals,shrub flowers          
##  Larrea,none,annual plant flowers - Larrea,annuals,shrub flowers            
##  Larrea,none,annual plant flowers - open,annuals,shrub flowers              
##  Larrea,none,annual plant flowers - Ambrosia,none,shrub flowers             
##  Larrea,none,annual plant flowers - Larrea,none,shrub flowers               
##  Larrea,none,annual plant flowers - open,none,shrub flowers                 
##  open,none,annual plant flowers - Ambrosia,annuals,shrub flowers            
##  open,none,annual plant flowers - Larrea,annuals,shrub flowers              
##  open,none,annual plant flowers - open,annuals,shrub flowers                
##  open,none,annual plant flowers - Ambrosia,none,shrub flowers               
##  open,none,annual plant flowers - Larrea,none,shrub flowers                 
##  open,none,annual plant flowers - open,none,shrub flowers                   
##  Ambrosia,annuals,shrub flowers - Larrea,annuals,shrub flowers              
##  Ambrosia,annuals,shrub flowers - open,annuals,shrub flowers                
##  Ambrosia,annuals,shrub flowers - Ambrosia,none,shrub flowers               
##  Ambrosia,annuals,shrub flowers - Larrea,none,shrub flowers                 
##  Ambrosia,annuals,shrub flowers - open,none,shrub flowers                   
##  Larrea,annuals,shrub flowers - open,annuals,shrub flowers                  
##  Larrea,annuals,shrub flowers - Ambrosia,none,shrub flowers                 
##  Larrea,annuals,shrub flowers - Larrea,none,shrub flowers                   
##  Larrea,annuals,shrub flowers - open,none,shrub flowers                     
##  open,annuals,shrub flowers - Ambrosia,none,shrub flowers                   
##  open,annuals,shrub flowers - Larrea,none,shrub flowers                     
##  open,annuals,shrub flowers - open,none,shrub flowers                       
##  Ambrosia,none,shrub flowers - Larrea,none,shrub flowers                    
##  Ambrosia,none,shrub flowers - open,none,shrub flowers                      
##  Larrea,none,shrub flowers - open,none,shrub flowers                        
##       estimate           SE df z.ratio p.value
##   1.301989e-05 6.934793e-06 NA   1.877  0.7735
##   1.115600e-05 7.093647e-06 NA   1.573  0.9191
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##  -1.013554e-03 8.868874e-05 NA -11.428  <.0001
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##  -1.013554e-03 8.696329e-05 NA -11.655  <.0001
##             NA           NA NA      NA      NA
##  -1.863888e-06 6.318099e-06 NA  -0.295  1.0000
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##  -1.026574e-03 8.653898e-05 NA -11.863  <.0001
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##  -1.026574e-03 8.476979e-05 NA -12.110  <.0001
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##  -1.024710e-03 8.716172e-05 NA -11.756  <.0001
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##  -1.024710e-03 8.540543e-05 NA -11.998  <.0001
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##  -4.068779e-19 5.025590e-05 NA   0.000  1.0000
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
##             NA           NA NA      NA      NA
## 
## P value adjustment: tukey method for comparing a family of 12 estimates
#2015
m <- glm(rate.per.flower~microsite*annuals*target.flowers + mean.floral.density, family = "gaussian", weight = net.time, data = freq.2015)
anova(m, test = "Chisq") 
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: rate.per.flower
## 
## Terms added sequentially (first to last)
## 
## 
##                                  Df Deviance Resid. Df Resid. Dev
## NULL                                               127   10303560
## microsite                         1   930543       126    9373017
## annuals                           1    81231       125    9291786
## target.flowers                    1   136011       124    9155775
## mean.floral.density               1  1645874       123    7509901
## microsite:annuals                 0        0       123    7509901
## microsite:target.flowers          0        0       123    7509901
## annuals:target.flowers            0        0       123    7509901
## microsite:annuals:target.flowers  0        0       123    7509901
##                                   Pr(>Chi)    
## NULL                                          
## microsite                        9.464e-05 ***
## annuals                             0.2487    
## target.flowers                      0.1356    
## mean.floral.density              2.081e-07 ***
## microsite:annuals                             
## microsite:target.flowers                      
## annuals:target.flowers                        
## microsite:annuals:target.flowers              
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lsmeans(m, pairwise~microsite*annuals*target.flowers, adjust="tukey")
## $lsmeans
##  microsite annuals target.flowers            lsmean        SE df
##  Larrea    annuals annual plant flowers -0.68462662 0.3286538 NA
##  open      annuals annual plant flowers -0.06415139 0.3215493 NA
##  Larrea    none    annual plant flowers          NA        NA NA
##  open      none    annual plant flowers          NA        NA NA
##  Larrea    annuals shrub flowers         0.88730664 0.4402202 NA
##  open      annuals shrub flowers                 NA        NA NA
##  Larrea    none    shrub flowers        -0.04304856 0.4298938 NA
##  open      none    shrub flowers                 NA        NA NA
##    asymp.LCL  asymp.UCL
##  -1.32877624 -0.0404770
##  -0.69437643  0.5660737
##           NA         NA
##           NA         NA
##   0.02449093  1.7501224
##           NA         NA
##  -0.88562500  0.7995279
##           NA         NA
## 
## Results are given on the identity (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                                                               
##  Larrea,annuals,annual plant flowers - open,annuals,annual plant flowers
##  Larrea,annuals,annual plant flowers - Larrea,none,annual plant flowers 
##  Larrea,annuals,annual plant flowers - open,none,annual plant flowers   
##  Larrea,annuals,annual plant flowers - Larrea,annuals,shrub flowers     
##  Larrea,annuals,annual plant flowers - open,annuals,shrub flowers       
##  Larrea,annuals,annual plant flowers - Larrea,none,shrub flowers        
##  Larrea,annuals,annual plant flowers - open,none,shrub flowers          
##  open,annuals,annual plant flowers - Larrea,none,annual plant flowers   
##  open,annuals,annual plant flowers - open,none,annual plant flowers     
##  open,annuals,annual plant flowers - Larrea,annuals,shrub flowers       
##  open,annuals,annual plant flowers - open,annuals,shrub flowers         
##  open,annuals,annual plant flowers - Larrea,none,shrub flowers          
##  open,annuals,annual plant flowers - open,none,shrub flowers            
##  Larrea,none,annual plant flowers - open,none,annual plant flowers      
##  Larrea,none,annual plant flowers - Larrea,annuals,shrub flowers        
##  Larrea,none,annual plant flowers - open,annuals,shrub flowers          
##  Larrea,none,annual plant flowers - Larrea,none,shrub flowers           
##  Larrea,none,annual plant flowers - open,none,shrub flowers             
##  open,none,annual plant flowers - Larrea,annuals,shrub flowers          
##  open,none,annual plant flowers - open,annuals,shrub flowers            
##  open,none,annual plant flowers - Larrea,none,shrub flowers             
##  open,none,annual plant flowers - open,none,shrub flowers               
##  Larrea,annuals,shrub flowers - open,annuals,shrub flowers              
##  Larrea,annuals,shrub flowers - Larrea,none,shrub flowers               
##  Larrea,annuals,shrub flowers - open,none,shrub flowers                 
##  open,annuals,shrub flowers - Larrea,none,shrub flowers                 
##  open,annuals,shrub flowers - open,none,shrub flowers                   
##  Larrea,none,shrub flowers - open,none,shrub flowers                    
##     estimate        SE df z.ratio p.value
##  -0.62047523 0.2377759 NA  -2.609  0.1523
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##  -1.57193326 0.6232618 NA  -2.522  0.1859
##           NA        NA NA      NA      NA
##  -0.64157806 0.5275629 NA  -1.216  0.9275
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##  -0.95145803 0.6219739 NA  -1.530  0.7915
##           NA        NA NA      NA      NA
##  -0.02110283 0.5226841 NA  -0.040  1.0000
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##   0.93035520 0.6220909 NA   1.496  0.8102
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
## 
## P value adjustment: tukey method for comparing a family of 8 estimates
#2016
m <- glm(rate.per.flower~microsite*annuals*target.flowers + mean.floral.density, family = "gaussian", weight = net.time, data = freq.2016)
anova(m, test = "Chisq") 
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: rate.per.flower
## 
## Terms added sequentially (first to last)
## 
## 
##                                  Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                                               137   16184928         
## microsite                         2    62422       135   16122506  0.75758
## annuals                           1   365376       134   15757130  0.07142
## target.flowers                    1   296327       133   15460803  0.10448
## mean.floral.density               1   621158       132   14839644  0.01874
## microsite:annuals                 0        0       132   14839644         
## microsite:target.flowers          0        0       132   14839644         
## annuals:target.flowers            0        0       132   14839644         
## microsite:annuals:target.flowers  0        0       132   14839644         
##                                   
## NULL                              
## microsite                         
## annuals                          .
## target.flowers                    
## mean.floral.density              *
## microsite:annuals                 
## microsite:target.flowers          
## annuals:target.flowers            
## microsite:annuals:target.flowers  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lsmeans(m, pairwise~microsite*annuals*target.flowers, adjust="tukey")
## $lsmeans
##  microsite annuals target.flowers           lsmean       SE df   asymp.LCL
##  Ambrosia  annuals annual plant flowers -0.4581064 1.195827 NA -2.80188475
##  Larrea    annuals annual plant flowers -0.3133599 1.106716 NA -2.48248325
##  open      annuals annual plant flowers -0.5009007 1.133545 NA -2.72260794
##  Ambrosia  none    annual plant flowers         NA       NA NA          NA
##  Larrea    none    annual plant flowers         NA       NA NA          NA
##  open      none    annual plant flowers         NA       NA NA          NA
##  Ambrosia  annuals shrub flowers                NA       NA NA          NA
##  Larrea    annuals shrub flowers         4.1316607 2.226652 NA -0.23249711
##  open      annuals shrub flowers                NA       NA NA          NA
##  Ambrosia  none    shrub flowers                NA       NA NA          NA
##  Larrea    none    shrub flowers         4.1364009 2.133640 NA -0.04545592
##  open      none    shrub flowers                NA       NA NA          NA
##  asymp.UCL
##   1.885672
##   1.855763
##   1.720806
##         NA
##         NA
##         NA
##         NA
##   8.495818
##         NA
##         NA
##   8.318258
##         NA
## 
## Results are given on the identity (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                                                                   
##  Ambrosia,annuals,annual plant flowers - Larrea,annuals,annual plant flowers
##  Ambrosia,annuals,annual plant flowers - open,annuals,annual plant flowers  
##  Ambrosia,annuals,annual plant flowers - Ambrosia,none,annual plant flowers 
##  Ambrosia,annuals,annual plant flowers - Larrea,none,annual plant flowers   
##  Ambrosia,annuals,annual plant flowers - open,none,annual plant flowers     
##  Ambrosia,annuals,annual plant flowers - Ambrosia,annuals,shrub flowers     
##  Ambrosia,annuals,annual plant flowers - Larrea,annuals,shrub flowers       
##  Ambrosia,annuals,annual plant flowers - open,annuals,shrub flowers         
##  Ambrosia,annuals,annual plant flowers - Ambrosia,none,shrub flowers        
##  Ambrosia,annuals,annual plant flowers - Larrea,none,shrub flowers          
##  Ambrosia,annuals,annual plant flowers - open,none,shrub flowers            
##  Larrea,annuals,annual plant flowers - open,annuals,annual plant flowers    
##  Larrea,annuals,annual plant flowers - Ambrosia,none,annual plant flowers   
##  Larrea,annuals,annual plant flowers - Larrea,none,annual plant flowers     
##  Larrea,annuals,annual plant flowers - open,none,annual plant flowers       
##  Larrea,annuals,annual plant flowers - Ambrosia,annuals,shrub flowers       
##  Larrea,annuals,annual plant flowers - Larrea,annuals,shrub flowers         
##  Larrea,annuals,annual plant flowers - open,annuals,shrub flowers           
##  Larrea,annuals,annual plant flowers - Ambrosia,none,shrub flowers          
##  Larrea,annuals,annual plant flowers - Larrea,none,shrub flowers            
##  Larrea,annuals,annual plant flowers - open,none,shrub flowers              
##  open,annuals,annual plant flowers - Ambrosia,none,annual plant flowers     
##  open,annuals,annual plant flowers - Larrea,none,annual plant flowers       
##  open,annuals,annual plant flowers - open,none,annual plant flowers         
##  open,annuals,annual plant flowers - Ambrosia,annuals,shrub flowers         
##  open,annuals,annual plant flowers - Larrea,annuals,shrub flowers           
##  open,annuals,annual plant flowers - open,annuals,shrub flowers             
##  open,annuals,annual plant flowers - Ambrosia,none,shrub flowers            
##  open,annuals,annual plant flowers - Larrea,none,shrub flowers              
##  open,annuals,annual plant flowers - open,none,shrub flowers                
##  Ambrosia,none,annual plant flowers - Larrea,none,annual plant flowers      
##  Ambrosia,none,annual plant flowers - open,none,annual plant flowers        
##  Ambrosia,none,annual plant flowers - Ambrosia,annuals,shrub flowers        
##  Ambrosia,none,annual plant flowers - Larrea,annuals,shrub flowers          
##  Ambrosia,none,annual plant flowers - open,annuals,shrub flowers            
##  Ambrosia,none,annual plant flowers - Ambrosia,none,shrub flowers           
##  Ambrosia,none,annual plant flowers - Larrea,none,shrub flowers             
##  Ambrosia,none,annual plant flowers - open,none,shrub flowers               
##  Larrea,none,annual plant flowers - open,none,annual plant flowers          
##  Larrea,none,annual plant flowers - Ambrosia,annuals,shrub flowers          
##  Larrea,none,annual plant flowers - Larrea,annuals,shrub flowers            
##  Larrea,none,annual plant flowers - open,annuals,shrub flowers              
##  Larrea,none,annual plant flowers - Ambrosia,none,shrub flowers             
##  Larrea,none,annual plant flowers - Larrea,none,shrub flowers               
##  Larrea,none,annual plant flowers - open,none,shrub flowers                 
##  open,none,annual plant flowers - Ambrosia,annuals,shrub flowers            
##  open,none,annual plant flowers - Larrea,annuals,shrub flowers              
##  open,none,annual plant flowers - open,annuals,shrub flowers                
##  open,none,annual plant flowers - Ambrosia,none,shrub flowers               
##  open,none,annual plant flowers - Larrea,none,shrub flowers                 
##  open,none,annual plant flowers - open,none,shrub flowers                   
##  Ambrosia,annuals,shrub flowers - Larrea,annuals,shrub flowers              
##  Ambrosia,annuals,shrub flowers - open,annuals,shrub flowers                
##  Ambrosia,annuals,shrub flowers - Ambrosia,none,shrub flowers               
##  Ambrosia,annuals,shrub flowers - Larrea,none,shrub flowers                 
##  Ambrosia,annuals,shrub flowers - open,none,shrub flowers                   
##  Larrea,annuals,shrub flowers - open,annuals,shrub flowers                  
##  Larrea,annuals,shrub flowers - Ambrosia,none,shrub flowers                 
##  Larrea,annuals,shrub flowers - Larrea,none,shrub flowers                   
##  Larrea,annuals,shrub flowers - open,none,shrub flowers                     
##  open,annuals,shrub flowers - Ambrosia,none,shrub flowers                   
##  open,annuals,shrub flowers - Larrea,none,shrub flowers                     
##  open,annuals,shrub flowers - open,none,shrub flowers                       
##  Ambrosia,none,shrub flowers - Larrea,none,shrub flowers                    
##  Ambrosia,none,shrub flowers - open,none,shrub flowers                      
##  Larrea,none,shrub flowers - open,none,shrub flowers                        
##     estimate        SE df z.ratio p.value
##  -0.14474648 0.2536787 NA  -0.571  1.0000
##   0.04279435 0.2594896 NA   0.165  1.0000
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##  -4.58976706 3.2442844 NA  -1.415  0.9610
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##  -4.59450732 3.1811665 NA  -1.444  0.9547
##           NA        NA NA      NA      NA
##   0.18754083 0.2311197 NA   0.811  0.9997
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##  -4.44502059 3.1656452 NA  -1.404  0.9631
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##  -4.44976085 3.1009271 NA  -1.435  0.9568
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##  -4.63256141 3.1884253 NA  -1.453  0.9528
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##  -4.63730167 3.1241791 NA  -1.484  0.9451
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##  -0.00474026 1.8383895 NA  -0.003  1.0000
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
## 
## P value adjustment: tukey method for comparing a family of 12 estimates
#2015
m <- glm(mean.insect.RTU~microsite*annuals*target.flowers + mean.floral.density, family = "gaussian", weight = net.time, data = freq.2015)
anova(m, test = "Chisq") 
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: mean.insect.RTU
## 
## Terms added sequentially (first to last)
## 
## 
##                                  Df Deviance Resid. Df Resid. Dev
## NULL                                               127   15398546
## microsite                         1  2824632       126   12573915
## annuals                           1   610534       125   11963380
## target.flowers                    1   696463       124   11266917
## mean.floral.density               1  1055518       123   10211399
## microsite:annuals                 0        0       123   10211399
## microsite:target.flowers          0        0       123   10211399
## annuals:target.flowers            0        0       123   10211399
## microsite:annuals:target.flowers  0        0       123   10211399
##                                   Pr(>Chi)    
## NULL                                          
## microsite                        5.444e-09 ***
## annuals                          0.0066910 ** 
## target.flowers                   0.0037747 ** 
## mean.floral.density              0.0003629 ***
## microsite:annuals                             
## microsite:target.flowers                      
## annuals:target.flowers                        
## microsite:annuals:target.flowers              
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lsmeans(m, pairwise~microsite*annuals*target.flowers, adjust="tukey")
## $lsmeans
##  microsite annuals target.flowers         lsmean        SE df asymp.LCL
##  Larrea    annuals annual plant flowers 4.138664 0.3832341 NA  3.387539
##  open      annuals annual plant flowers 5.139401 0.3749498 NA  4.404513
##  Larrea    none    annual plant flowers       NA        NA NA        NA
##  open      none    annual plant flowers       NA        NA NA        NA
##  Larrea    annuals shrub flowers        4.416993 0.5133286 NA  3.410888
##  open      annuals shrub flowers              NA        NA NA        NA
##  Larrea    none    shrub flowers        3.461012 0.5012873 NA  2.478506
##  open      none    shrub flowers              NA        NA NA        NA
##  asymp.UCL
##   4.889789
##   5.874289
##         NA
##         NA
##   5.423099
##         NA
##   4.443517
##         NA
## 
## Results are given on the identity (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                                                               
##  Larrea,annuals,annual plant flowers - open,annuals,annual plant flowers
##  Larrea,annuals,annual plant flowers - Larrea,none,annual plant flowers 
##  Larrea,annuals,annual plant flowers - open,none,annual plant flowers   
##  Larrea,annuals,annual plant flowers - Larrea,annuals,shrub flowers     
##  Larrea,annuals,annual plant flowers - open,annuals,shrub flowers       
##  Larrea,annuals,annual plant flowers - Larrea,none,shrub flowers        
##  Larrea,annuals,annual plant flowers - open,none,shrub flowers          
##  open,annuals,annual plant flowers - Larrea,none,annual plant flowers   
##  open,annuals,annual plant flowers - open,none,annual plant flowers     
##  open,annuals,annual plant flowers - Larrea,annuals,shrub flowers       
##  open,annuals,annual plant flowers - open,annuals,shrub flowers         
##  open,annuals,annual plant flowers - Larrea,none,shrub flowers          
##  open,annuals,annual plant flowers - open,none,shrub flowers            
##  Larrea,none,annual plant flowers - open,none,annual plant flowers      
##  Larrea,none,annual plant flowers - Larrea,annuals,shrub flowers        
##  Larrea,none,annual plant flowers - open,annuals,shrub flowers          
##  Larrea,none,annual plant flowers - Larrea,none,shrub flowers           
##  Larrea,none,annual plant flowers - open,none,shrub flowers             
##  open,none,annual plant flowers - Larrea,annuals,shrub flowers          
##  open,none,annual plant flowers - open,annuals,shrub flowers            
##  open,none,annual plant flowers - Larrea,none,shrub flowers             
##  open,none,annual plant flowers - open,none,shrub flowers               
##  Larrea,annuals,shrub flowers - open,annuals,shrub flowers              
##  Larrea,annuals,shrub flowers - Larrea,none,shrub flowers               
##  Larrea,annuals,shrub flowers - open,none,shrub flowers                 
##  open,annuals,shrub flowers - Larrea,none,shrub flowers                 
##  open,annuals,shrub flowers - open,none,shrub flowers                   
##  Larrea,none,shrub flowers - open,none,shrub flowers                    
##    estimate        SE df z.ratio p.value
##  -1.0007378 0.2772639 NA  -3.609  0.0074
##          NA        NA NA      NA      NA
##          NA        NA NA      NA      NA
##  -0.2783295 0.7267684 NA  -0.383  0.9999
##          NA        NA NA      NA      NA
##   0.6776521 0.6151765 NA   1.102  0.9567
##          NA        NA NA      NA      NA
##          NA        NA NA      NA      NA
##          NA        NA NA      NA      NA
##   0.7224082 0.7252666 NA   0.996  0.9751
##          NA        NA NA      NA      NA
##   1.6783899 0.6094874 NA   2.754  0.1069
##          NA        NA NA      NA      NA
##          NA        NA NA      NA      NA
##          NA        NA NA      NA      NA
##          NA        NA NA      NA      NA
##          NA        NA NA      NA      NA
##          NA        NA NA      NA      NA
##          NA        NA NA      NA      NA
##          NA        NA NA      NA      NA
##          NA        NA NA      NA      NA
##          NA        NA NA      NA      NA
##          NA        NA NA      NA      NA
##   0.9559816 0.7254031 NA   1.318  0.8924
##          NA        NA NA      NA      NA
##          NA        NA NA      NA      NA
##          NA        NA NA      NA      NA
##          NA        NA NA      NA      NA
## 
## P value adjustment: tukey method for comparing a family of 8 estimates
#2016
m <- glm(mean.insect.RTU~microsite*annuals*target.flowers + mean.floral.density, family = "gaussian", weight = net.time, data = freq.2016)
anova(m, test = "Chisq") 
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: mean.insect.RTU
## 
## Terms added sequentially (first to last)
## 
## 
##                                  Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                                               137   23629226         
## microsite                         2   100490       135   23528735  0.74660
## annuals                           1     8546       134   23520189  0.82358
## target.flowers                    1   143618       133   23376571  0.36075
## mean.floral.density               1   680872       132   22695699  0.04659
## microsite:annuals                 0        0       132   22695699         
## microsite:target.flowers          0        0       132   22695699         
## annuals:target.flowers            0        0       132   22695699         
## microsite:annuals:target.flowers  0        0       132   22695699         
##                                   
## NULL                              
## microsite                         
## annuals                           
## target.flowers                    
## mean.floral.density              *
## microsite:annuals                 
## microsite:target.flowers          
## annuals:target.flowers            
## microsite:annuals:target.flowers  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lsmeans(m, pairwise~microsite*annuals*target.flowers, adjust="tukey")
## $lsmeans
##  microsite annuals target.flowers          lsmean       SE df  asymp.LCL
##  Ambrosia  annuals annual plant flowers 0.7689305 1.478865 NA -2.1295913
##  Larrea    annuals annual plant flowers 0.7909201 1.368662 NA -1.8916080
##  open      annuals annual plant flowers 0.7274031 1.401841 NA -2.0201549
##  Ambrosia  none    annual plant flowers        NA       NA NA         NA
##  Larrea    none    annual plant flowers        NA       NA NA         NA
##  open      none    annual plant flowers        NA       NA NA         NA
##  Ambrosia  annuals shrub flowers               NA       NA NA         NA
##  Larrea    annuals shrub flowers        6.2329659 2.753673 NA  0.8358657
##  open      annuals shrub flowers               NA       NA NA         NA
##  Ambrosia  none    shrub flowers               NA       NA NA         NA
##  Larrea    none    shrub flowers        7.4396759 2.638646 NA  2.2680250
##  open      none    shrub flowers               NA       NA NA         NA
##  asymp.UCL
##   3.667452
##   3.473448
##   3.474961
##         NA
##         NA
##         NA
##         NA
##  11.630066
##         NA
##         NA
##  12.611327
##         NA
## 
## Results are given on the identity (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                                                                   
##  Ambrosia,annuals,annual plant flowers - Larrea,annuals,annual plant flowers
##  Ambrosia,annuals,annual plant flowers - open,annuals,annual plant flowers  
##  Ambrosia,annuals,annual plant flowers - Ambrosia,none,annual plant flowers 
##  Ambrosia,annuals,annual plant flowers - Larrea,none,annual plant flowers   
##  Ambrosia,annuals,annual plant flowers - open,none,annual plant flowers     
##  Ambrosia,annuals,annual plant flowers - Ambrosia,annuals,shrub flowers     
##  Ambrosia,annuals,annual plant flowers - Larrea,annuals,shrub flowers       
##  Ambrosia,annuals,annual plant flowers - open,annuals,shrub flowers         
##  Ambrosia,annuals,annual plant flowers - Ambrosia,none,shrub flowers        
##  Ambrosia,annuals,annual plant flowers - Larrea,none,shrub flowers          
##  Ambrosia,annuals,annual plant flowers - open,none,shrub flowers            
##  Larrea,annuals,annual plant flowers - open,annuals,annual plant flowers    
##  Larrea,annuals,annual plant flowers - Ambrosia,none,annual plant flowers   
##  Larrea,annuals,annual plant flowers - Larrea,none,annual plant flowers     
##  Larrea,annuals,annual plant flowers - open,none,annual plant flowers       
##  Larrea,annuals,annual plant flowers - Ambrosia,annuals,shrub flowers       
##  Larrea,annuals,annual plant flowers - Larrea,annuals,shrub flowers         
##  Larrea,annuals,annual plant flowers - open,annuals,shrub flowers           
##  Larrea,annuals,annual plant flowers - Ambrosia,none,shrub flowers          
##  Larrea,annuals,annual plant flowers - Larrea,none,shrub flowers            
##  Larrea,annuals,annual plant flowers - open,none,shrub flowers              
##  open,annuals,annual plant flowers - Ambrosia,none,annual plant flowers     
##  open,annuals,annual plant flowers - Larrea,none,annual plant flowers       
##  open,annuals,annual plant flowers - open,none,annual plant flowers         
##  open,annuals,annual plant flowers - Ambrosia,annuals,shrub flowers         
##  open,annuals,annual plant flowers - Larrea,annuals,shrub flowers           
##  open,annuals,annual plant flowers - open,annuals,shrub flowers             
##  open,annuals,annual plant flowers - Ambrosia,none,shrub flowers            
##  open,annuals,annual plant flowers - Larrea,none,shrub flowers              
##  open,annuals,annual plant flowers - open,none,shrub flowers                
##  Ambrosia,none,annual plant flowers - Larrea,none,annual plant flowers      
##  Ambrosia,none,annual plant flowers - open,none,annual plant flowers        
##  Ambrosia,none,annual plant flowers - Ambrosia,annuals,shrub flowers        
##  Ambrosia,none,annual plant flowers - Larrea,annuals,shrub flowers          
##  Ambrosia,none,annual plant flowers - open,annuals,shrub flowers            
##  Ambrosia,none,annual plant flowers - Ambrosia,none,shrub flowers           
##  Ambrosia,none,annual plant flowers - Larrea,none,shrub flowers             
##  Ambrosia,none,annual plant flowers - open,none,shrub flowers               
##  Larrea,none,annual plant flowers - open,none,annual plant flowers          
##  Larrea,none,annual plant flowers - Ambrosia,annuals,shrub flowers          
##  Larrea,none,annual plant flowers - Larrea,annuals,shrub flowers            
##  Larrea,none,annual plant flowers - open,annuals,shrub flowers              
##  Larrea,none,annual plant flowers - Ambrosia,none,shrub flowers             
##  Larrea,none,annual plant flowers - Larrea,none,shrub flowers               
##  Larrea,none,annual plant flowers - open,none,shrub flowers                 
##  open,none,annual plant flowers - Ambrosia,annuals,shrub flowers            
##  open,none,annual plant flowers - Larrea,annuals,shrub flowers              
##  open,none,annual plant flowers - open,annuals,shrub flowers                
##  open,none,annual plant flowers - Ambrosia,none,shrub flowers               
##  open,none,annual plant flowers - Larrea,none,shrub flowers                 
##  open,none,annual plant flowers - open,none,shrub flowers                   
##  Ambrosia,annuals,shrub flowers - Larrea,annuals,shrub flowers              
##  Ambrosia,annuals,shrub flowers - open,annuals,shrub flowers                
##  Ambrosia,annuals,shrub flowers - Ambrosia,none,shrub flowers               
##  Ambrosia,annuals,shrub flowers - Larrea,none,shrub flowers                 
##  Ambrosia,annuals,shrub flowers - open,none,shrub flowers                   
##  Larrea,annuals,shrub flowers - open,annuals,shrub flowers                  
##  Larrea,annuals,shrub flowers - Ambrosia,none,shrub flowers                 
##  Larrea,annuals,shrub flowers - Larrea,none,shrub flowers                   
##  Larrea,annuals,shrub flowers - open,none,shrub flowers                     
##  open,annuals,shrub flowers - Ambrosia,none,shrub flowers                   
##  open,annuals,shrub flowers - Larrea,none,shrub flowers                     
##  open,annuals,shrub flowers - open,none,shrub flowers                       
##  Ambrosia,none,shrub flowers - Larrea,none,shrub flowers                    
##  Ambrosia,none,shrub flowers - open,none,shrub flowers                      
##  Larrea,none,shrub flowers - open,none,shrub flowers                        
##     estimate        SE df z.ratio p.value
##  -0.02198958 0.3137213 NA  -0.070  1.0000
##   0.04152741 0.3209077 NA   0.129  1.0000
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##  -5.46403541 4.0121666 NA  -1.362  0.9705
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##  -6.67074537 3.9341095 NA  -1.696  0.8707
##           NA        NA NA      NA      NA
##   0.06351699 0.2858228 NA   0.222  1.0000
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##  -5.44204583 3.9149145 NA  -1.390  0.9657
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##  -6.64875579 3.8348783 NA  -1.734  0.8528
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##  -5.50556282 3.9430863 NA  -1.396  0.9645
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##  -6.71227278 3.8636338 NA  -1.737  0.8511
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##  -1.20670996 2.2735137 NA  -0.531  1.0000
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
##           NA        NA NA      NA      NA
## 
## P value adjustment: tukey method for comparing a family of 12 estimates
#Then test RTU nested within rep

#Then test in ONE model, frequency, with year as a factor.

Interpretation

Net.treatment models
1. Using the simplest models with all treatment levels aggregated but split by years, larrea is the most important magnet for frequency of visits (weighting for net.time recorded and using floral density as a covariate). This is a reasonable test because it captures enough of the variation and address non-orthogonality levels.
2. Mean visitation rate in 2015 significantly differed between larea with flowers and open and larea with flowers and annuals versus larrea without. This strongly suggests a double-magnet effect this season. In 2016, larrea flowering with annuals was also significantly greater than open microsites with annuals.

  1. Double-magnet supported for frequency too.

  2. Then check each prediction. Need to think over how to handle double-magnet H.